What are AI evaluation tools?
AI evaluation tools fall into four categories: developer eval frameworks that score models in CI, the AI vendors' own built-in evals, public benchmarks and leaderboards, and owned business-outcome evaluation. Most of what ranks for the term is the first category. Only the last answers "is this AI worth it to my business," and keeps the answer private.
Developer eval frameworks
Platforms like Braintrust, Arize, Galileo, LangSmith, and open-source libraries are built for ML engineers to test models and agents before shipping: prompt regression, hallucination checks, latency, token metrics, tracing. They are the right tool for developer-time quality, and VPC or SOC2 deployment is table stakes here. What they are not is a business-value measurement for an executive, and they are not designed to keep your evaluation criteria private from the model vendor.
The AI vendors' built-in evals
The hyperscalers and model providers ship evaluation and ROI dashboards inside your own tenant, for free, to drive consumption. They are convenient and conflicted by construction, because the party selling you the AI is grading it and can see exactly what you optimize for. See vendor evals vs. owned evals.
Public benchmarks and leaderboards
Benchmarks measure general capability on shared tasks. Useful for tracking the frontier, useless for your business, and because they are public, every model is trained to beat them, so they commoditize fast. Your outcomes are not on any leaderboard, and a public target gets gamed.
What is owned, business-outcome evaluation?
Owned, business-outcome evaluation is a distinct category that sits above developer eval tools rather than competing with them: it measures your business outcomes on your data, against criteria you define, on infrastructure you own and keep invisible to the vendors you evaluate. It often uses the dev tools as substrate, self-hosted inside your perimeter so even the tooling vendor never sees your criteria, and it is transferred to your team to run.
The categories, side by side
| Dev eval frameworks | Vendor built-in evals | Owned business evaluation | |
|---|---|---|---|
| Buyer | ML engineers | Whoever the vendor upsells | The business (CFO / CIO) |
| Measures | Model and token metrics | What renews the contract | Your business outcomes |
| Conflict of interest | Low | High | None; sells no AI |
| Criteria stay private | Not the goal | No | Yes, invisible to vendors |
| You own it | You rent the platform | No | Yes, transferred to you |
How should you choose or compare AI models?
You should not choose or compare AI models on a public leaderboard, which is gamed and contaminated, or on a vendor's own comparison, which is conflicted. The criterion that actually decides is your own evaluation, run on your own data against your own criteria, and re-scored on every model swap. The leaderboard tells you what is generally capable; only your own eval tells you what is right for you.
Model choice is downstream of owned evaluation, not benchmarks. We cover why public scores cannot be trusted in Goodhart's Law for AI, and the private benchmark you compare on in golden datasets.
Which do you need?
If you are an ML engineer shipping a model, a dev eval framework. If you are an executive deciding whether millions in AI spend is paying off, none of the first three, because none of them give you a trustworthy, private, business number. That is the category PrivateEval builds, and what private AI evaluation is defines it in full. If you would rather a partner build and transfer that capability than run a tool yourself, see AI evaluation services.